File size: 10,722 Bytes
e7d5680 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
from copy import deepcopy
import colossalai
import torch
import torch.distributed as dist
import wandb
from colossalai.booster import Booster
from colossalai.booster.plugin import LowLevelZeroPlugin
from colossalai.cluster import DistCoordinator
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device
from tqdm import tqdm
from opensora.acceleration.checkpoint import set_grad_checkpoint
from opensora.acceleration.parallel_states import (
get_data_parallel_group,
set_data_parallel_group,
set_sequence_parallel_group,
)
from opensora.acceleration.plugin import ZeroSeqParallelPlugin
from opensora.datasets import DatasetFromCSV, get_transforms_image, get_transforms_video, prepare_dataloader
from opensora.registry import MODELS, SCHEDULERS, build_module
from opensora.utils.ckpt_utils import create_logger, load, model_sharding, record_model_param_shape, save
from opensora.utils.config_utils import (
create_experiment_workspace,
create_tensorboard_writer,
parse_configs,
save_training_config,
)
from opensora.utils.misc import all_reduce_mean, format_numel_str, get_model_numel, requires_grad, to_torch_dtype
from opensora.utils.train_utils import update_ema
def main():
# ======================================================
# 1. args & cfg
# ======================================================
cfg = parse_configs(training=True)
print(cfg)
exp_name, exp_dir = create_experiment_workspace(cfg)
save_training_config(cfg._cfg_dict, exp_dir)
# ======================================================
# 2. runtime variables & colossalai launch
# ======================================================
assert torch.cuda.is_available(), "Training currently requires at least one GPU."
assert cfg.dtype in ["fp16", "bf16"], f"Unknown mixed precision {cfg.dtype}"
# 2.1. colossalai init distributed training
colossalai.launch_from_torch({})
coordinator = DistCoordinator()
device = get_current_device()
dtype = to_torch_dtype(cfg.dtype)
# 2.2. init logger, tensorboard & wandb
if not coordinator.is_master():
logger = create_logger(None)
else:
logger = create_logger(exp_dir)
logger.info(f"Experiment directory created at {exp_dir}")
writer = create_tensorboard_writer(exp_dir)
if cfg.wandb:
wandb.init(project="minisora", name=exp_name, config=cfg._cfg_dict)
# 2.3. initialize ColossalAI booster
if cfg.plugin == "zero2":
plugin = LowLevelZeroPlugin(
stage=2,
precision=cfg.dtype,
initial_scale=2**16,
max_norm=cfg.grad_clip,
)
set_data_parallel_group(dist.group.WORLD)
elif cfg.plugin == "zero2-seq":
plugin = ZeroSeqParallelPlugin(
sp_size=cfg.sp_size,
stage=2,
precision=cfg.dtype,
initial_scale=2**16,
max_norm=cfg.grad_clip,
)
set_sequence_parallel_group(plugin.sp_group)
set_data_parallel_group(plugin.dp_group)
else:
raise ValueError(f"Unknown plugin {cfg.plugin}")
booster = Booster(plugin=plugin)
# ======================================================
# 3. build dataset and dataloader
# ======================================================
dataset = DatasetFromCSV(
cfg.data_path,
# TODO: change transforms
transform=(
get_transforms_video(cfg.image_size[0])
if not cfg.use_image_transform
else get_transforms_image(cfg.image_size[0])
),
num_frames=cfg.num_frames,
frame_interval=cfg.frame_interval,
root=cfg.root,
)
# TODO: use plugin's prepare dataloader
# a batch contains:
# {
# "video": torch.Tensor, # [B, C, T, H, W],
# "text": List[str],
# }
dataloader = prepare_dataloader(
dataset,
batch_size=cfg.batch_size,
num_workers=cfg.num_workers,
shuffle=True,
drop_last=True,
pin_memory=True,
process_group=get_data_parallel_group(),
)
logger.info(f"Dataset contains {len(dataset):,} videos ({cfg.data_path})")
total_batch_size = cfg.batch_size * dist.get_world_size() // cfg.sp_size
logger.info(f"Total batch size: {total_batch_size}")
# ======================================================
# 4. build model
# ======================================================
# 4.1. build model
input_size = (cfg.num_frames, *cfg.image_size)
vae = build_module(cfg.vae, MODELS)
latent_size = vae.get_latent_size(input_size)
text_encoder = build_module(cfg.text_encoder, MODELS, device=device) # T5 must be fp32
model = build_module(
cfg.model,
MODELS,
input_size=latent_size,
in_channels=vae.out_channels,
caption_channels=text_encoder.output_dim,
model_max_length=text_encoder.model_max_length,
dtype=dtype,
)
model_numel, model_numel_trainable = get_model_numel(model)
logger.info(
f"Trainable model params: {format_numel_str(model_numel_trainable)}, Total model params: {format_numel_str(model_numel)}"
)
# 4.2. create ema
ema = deepcopy(model).to(torch.float32).to(device)
requires_grad(ema, False)
ema_shape_dict = record_model_param_shape(ema)
# 4.3. move to device
vae = vae.to(device, dtype)
model = model.to(device, dtype)
# 4.4. build scheduler
scheduler = build_module(cfg.scheduler, SCHEDULERS)
# 4.5. setup optimizer
optimizer = HybridAdam(
filter(lambda p: p.requires_grad, model.parameters()), lr=cfg.lr, weight_decay=0, adamw_mode=True
)
lr_scheduler = None
# 4.6. prepare for training
if cfg.grad_checkpoint:
set_grad_checkpoint(model)
model.train()
update_ema(ema, model, decay=0, sharded=False)
ema.eval()
# =======================================================
# 5. boost model for distributed training with colossalai
# =======================================================
torch.set_default_dtype(dtype)
model, optimizer, _, dataloader, lr_scheduler = booster.boost(
model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, dataloader=dataloader
)
torch.set_default_dtype(torch.float)
num_steps_per_epoch = len(dataloader)
logger.info("Boost model for distributed training")
# =======================================================
# 6. training loop
# =======================================================
start_epoch = start_step = log_step = sampler_start_idx = 0
running_loss = 0.0
# 6.1. resume training
if cfg.load is not None:
logger.info("Loading checkpoint")
start_epoch, start_step, sampler_start_idx = load(booster, model, ema, optimizer, lr_scheduler, cfg.load)
logger.info(f"Loaded checkpoint {cfg.load} at epoch {start_epoch} step {start_step}")
logger.info(f"Training for {cfg.epochs} epochs with {num_steps_per_epoch} steps per epoch")
dataloader.sampler.set_start_index(sampler_start_idx)
model_sharding(ema)
# 6.2. training loop
for epoch in range(start_epoch, cfg.epochs):
dataloader.sampler.set_epoch(epoch)
dataloader_iter = iter(dataloader)
logger.info(f"Beginning epoch {epoch}...")
with tqdm(
range(start_step, num_steps_per_epoch),
desc=f"Epoch {epoch}",
disable=not coordinator.is_master(),
total=num_steps_per_epoch,
initial=start_step,
) as pbar:
for step in pbar:
batch = next(dataloader_iter)
x = batch["video"].to(device, dtype) # [B, C, T, H, W]
y = batch["text"]
with torch.no_grad():
# Prepare visual inputs
x = vae.encode(x) # [B, C, T, H/P, W/P]
# Prepare text inputs
model_args = text_encoder.encode(y)
# Diffusion
t = torch.randint(0, scheduler.num_timesteps, (x.shape[0],), device=device)
loss_dict = scheduler.training_losses(model, x, t, model_args)
# Backward & update
loss = loss_dict["loss"].mean()
booster.backward(loss=loss, optimizer=optimizer)
optimizer.step()
optimizer.zero_grad()
# Update EMA
update_ema(ema, model.module, optimizer=optimizer)
# Log loss values:
all_reduce_mean(loss)
running_loss += loss.item()
global_step = epoch * num_steps_per_epoch + step
log_step += 1
# Log to tensorboard
if coordinator.is_master() and (global_step + 1) % cfg.log_every == 0:
avg_loss = running_loss / log_step
pbar.set_postfix({"loss": avg_loss, "step": step, "global_step": global_step})
running_loss = 0
log_step = 0
writer.add_scalar("loss", loss.item(), global_step)
if cfg.wandb:
wandb.log(
{
"iter": global_step,
"num_samples": global_step * total_batch_size,
"epoch": epoch,
"loss": loss.item(),
"avg_loss": avg_loss,
},
step=global_step,
)
# Save checkpoint
if cfg.ckpt_every > 0 and (global_step + 1) % cfg.ckpt_every == 0:
save(
booster,
model,
ema,
optimizer,
lr_scheduler,
epoch,
step + 1,
global_step + 1,
cfg.batch_size,
coordinator,
exp_dir,
ema_shape_dict,
)
logger.info(
f"Saved checkpoint at epoch {epoch} step {step + 1} global_step {global_step + 1} to {exp_dir}"
)
# the continue epochs are not resumed, so we need to reset the sampler start index and start step
dataloader.sampler.set_start_index(0)
start_step = 0
if __name__ == "__main__":
main()
|